The Bayes Bootstrap method is widely used in the field of small sample prediction. However, due to the random value points that are not conducive to the prediction accuracy in the randomly generated self-service sample, the prediction deviation is large. In view of this deficiency, this paper proposes the Bayes Bootstrap & k-means method. In the case of having small sample failure data, use the Bayes Bootstrap method to generate self-service samples to expand the capacity of the original life data firstly, and then use the k-means method to perform data clustering analysis to remove outliers as much as possible and filter out more data points that meet the forecasting rules. Finally, the multi-chip module interconnection structure double stress accelerated life prediction is used as an example to verify the calculation method. Compared with the Bayes Bootstrap method, the prediction accuracy is improved by about 81. 44%, which has certain engineering significance.